1. Field of the Invention
This invention generally relates to a method and apparatus for trajectory control of robot manipulators or the, like, and more specifically relates to an inverse dynamics based robot controller and a method relating to the same.
2. Description of the Prior Art
The dynamic equations of motion of rigid-body robot manipulators have been derived using Lagrange-Euler equations, Newton-Euler equations, generalized d'Alembert equations, Recursive-Lagrange equations, and Kane's equations. A number of algorithms have also been developed to compute the equations of motion in numeric and symbolic forms. The development of inverse dynamics algorithms- for open-loop and closed-loop manipulators that are computationally efficient has been another area of intensive investigations. A number of researchers have proposed to "customize" the dynamics equations of manipulators in order to reduce the computational requirements.
Once the dynamic equations of motion are obtained, appropriate control laws or strategies must be determined in order to achieve the desired system response and performance. During the past two decades, the control problem has been studied extensively, and a number of schemes have been proposed. It is generally acknowledged that fixed-gain, linear controllers do not provide adequate dynamic performance at high speeds for multi-degrees-of-freedom robot manipulators. Of the numerous schemes investigated to date, those involving the calculation of the actuator torques (forces) using an inverse dynamics model of the manipulator (sometimes called the computed torque method), and those applying adaptive control techniques, have been extensively studied, and probably show the greatest promise.
Most conventional model based robot manipulator control methods use the inverse dynamics of the robot arm to produce the main torque (force) component necessary for trajectory tracking. This effectively renders the non-linear model of the robot manipulator linear, and a linear controller, usually PD (proportional+derivative) or PID (proportional+integral+derivative), is then used to provide any corrective torque (force) needed for tracking.
The conventional inverse dynamics based robot control methods have generally proven effective in reducing tracking errors in robot manipulators. However, the model based control methods currently available generally present three major drawbacks. Firstly, they all suffer from a heavy computational burden, and their real time implementation is only possible in a powerful computational environment. It is generally agreed that for high speed operation, the needed computational power for fast sampling rates desired in the control loops is still for the most part beyond the capabilities of today's microprocessors.
The second drawback of conventional model based control schemes is that the tracking accuracy is dependent on the accuracy with which the model parameters are determined. The model parameters have been shown to be difficult to be accurately determined, especially when kinematically complex manipulators are involved.
The third drawback is that the nonlinear dynamic model, particularly if used in a highly time-efficient form, is closely dependent on the manipulator being modeled and, therefore, the controller using this model requires significant modification in order to adapt to a different robot or to accommodate a new tool or payload.